期刊文献+

一种新颖的基于量化概念格的属性归纳算法 被引量:2

Novel Attribute Induction Algorithm Based on Quantized Concept Lattice
下载PDF
导出
摘要 为了解决数据挖掘过程中挖掘的知识粒度过粗或过细问题,并利用概念格的偏序特性,提出了一种基于量化概念格的属性归纳算法.首先对概念格的外延进行量化,得到量化概念格,再根据概念格的哈斯图,采用概念的爬升进行相应的泛化,从而获得基于量化概念格的多层、多属性归纳.与面向属性归纳(AOI)算法相比较,结果表明所提算法不仅能实现AOI的单一属性归纳,还能进行多层、多属性的归纳,其属性泛化的路径不是惟一的,并且很容易在量化概念格的哈斯图中寻找合适的泛化路径和阈值,以此得到用户要求的、合理的属性归纳结果. In order to deal with the problem of over-coarse or over-fine knowledge granularity in data mining, an attribute induction algorithm based on quantized concept lattice is proposed by using the partial property of the concept lattice. Firstly, the quantized concept lattice is defined by quantifying concept extension of the concept lattice, and then it is generalized using concept ascension according to the Hasse diagram of the concept lattice so as to get the induction with multilevel and multi-attribute based on the quantized concept lattice. Compared with the attribute-ori ented induction (AOI) algorithm, the proposed algorithm can not only perform the unitary induction of AOI, but also carry out the induction with multi-level and multi-attribute, and the path of attribute generalization is not unique. Moreover, it is easy to find proper generalized paths and thresholds in Hasse diagram of quantized concept lattice to obtain the reasonable results required by users.
出处 《西安交通大学学报》 EI CAS CSCD 北大核心 2007年第2期176-179,共4页 Journal of Xi'an Jiaotong University
基金 国家自然科学基金资助项目(60573174) 安徽省自然科学基金资助项目(050420207)
关键词 面向属性归纳 概念格 数据挖掘 attribute-oriented induction concept lattice data mining
  • 相关文献

参考文献12

  • 1Han Jiawei,Cai Yangdong,Cercone N.Knowledge discovery in databases:an attribute-oriented approach[EB/OL].[2006-06-06].http:∥citeseer.ist.psu.edu/cache/papers/cs/5021/han92knowledge.pdf. 被引量:1
  • 2Han Jiawei,Cai Yangdong,Cercone N.Data-driven discovery of quantitative rules in relation databases[J].IEEE Transactions on Knowledge and Data Engineering,1993,5(1):29-40. 被引量:1
  • 3Carter C L,Hamilton H J.Performance evaluation of attribute-oriented algorithms for knowledge discovery from databases[C]∥Proceedings of 7th IEEE International Conference Tools with Artificial Intelligence.Los Alamitos,USA:IEEE Computer Society,1995:486-489. 被引量:1
  • 4Carter C L,Hamilton H J.Efficient attribute-oriented generalization for knowledge discovery from large databases[J].IEEE Transactions on Knowledge and Data Engineering,1998,10(2):193-208. 被引量:1
  • 5陈红梅,王丽珍.面向属性的量化归纳[J].计算机研究与发展,2001,38(2):150-156. 被引量:8
  • 6周生炳,张钹,成栋.基于规则面向属性的数据库归纳的无回溯算法[J].软件学报,1999,10(7):673-678. 被引量:13
  • 7刘明吉,王秀峰,李宝林.基于多层次概念提升的知识发现方法[J].计算机科学,2001,28(3):109-111. 被引量:8
  • 8Wille R.Restructuring lattice theory:an approach based on hierarchies on concepts,in ordered sets[M].Dordrecht,Netherlands:Reidel,1982:445-470. 被引量:1
  • 9Wang Dexing,Hu Xuegang,Wang Hao.The research on model of mining association rules based on quantitative concept lattice[C]∥IEEE Proceedings of the 1st International Conference on Machine Learning and Cybernetics.Piscataway,USA:IEEE,2002:134-138. 被引量:1
  • 10强宇,刘宗田,林炜,时百胜,李云.模糊概念格在知识发现的应用及一种构造算法[J].电子学报,2005,33(2):350-353. 被引量:21

二级参考文献25

  • 1孟海军,李德毅.KDD中基于LAM的概念提升[J].计算机科学,1996,23(2):41-45. 被引量:4
  • 2姚卿达 张俊欣.KDD中数据预处理的研究.第十五届全国数据库学术会议论文集[M].南京,1998.137-138. 被引量:1
  • 3孙增圻.智能控制理论与技术[M].北京,广西:清华大学出版社,广西科学技术出版社,2000.. 被引量:35
  • 4Chen M,IEEE Trans Knowledge Data Engineering,1996年,8卷,6期,866页 被引量:1
  • 5Han J,IEEE Trans Knowledge Data Engineering,1996年,8卷,3期,373页 被引量:1
  • 6Han J,Advances in Knowledge Discovery and Data Mining,1996年,399页 被引量:1
  • 7Huang Y,Proc 1st International Conference on Knowledge Discovery and Data Mining,1995年,168页 被引量:1
  • 8Hu X,Computational Intelligence,1995年,11卷,2期,323页 被引量:1
  • 9Cheung D W,Methodologies for Intelligent Systems: 8th International Symposium,1994年,164页 被引量:1
  • 10Han J,Proc KDD’94: the AAAI’94 Workshop on Know ledge Discovery in Databases. AAAI TechnicalReport, WS-94-03,1994年,157页 被引量:1

共引文献33

同被引文献12

引证文献2

二级引证文献39

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部